4 ms·

So, I went directly into data science after an econ degree, worked there for 2 years and then transitioned to SWE (at startups). First, I am 100% certain ML wil

So, I went directly into data science after an econ degree, worked there for 2 years and then transitioned to SWE (at startups). First, I am 100% certain ML will become a part of the standard SWE toolkit (just like apis, docker, sql, etc..). However, to the relative "hardness" I would say ML currently is much less things but they can be really hard to get your head around (like starting to think in embeddings and vectors), and many APIs are wonky, because they are new. And the whole data science workflow space it in an early spot (we are still missing a "create-data-app", simmilar to the create-react-app.

antisthenes 5y ago
Your career path seems really interesting. Do you have a blog or some kind of work showcase where we can follow your journey and work?
- lysecret 5y ago
  happy to hear that. I fell in the trap of "to write a blog i have to code it myself haha" but il get back to you when its public:)
borroka 5y ago
Standard ML applications will be commoditized in the same way that creating a website has been commoditized. But for anything that isn't standard, and that's the vast majority of what businesses need, ML isn't going to become part of SWE's standard toolkit anytime soon. ML is kind of like tennis in the sense that you look at Nadal and Federer and all the greats and you can say, "man, I could do that," but you can't, not even close in this life and the next after this one. In fact, most SWEs who have developed some sort of "intuition" about ML have been quite dangerous, as they tend to be condescending to the real experts, have built things that make no sense at all or fall apart when 1 data point our of 100,000 changes, and when presented with the fact that they have no clue about ML, resort to the "the AI is all hype anyway" comeback. And vice versa (ML practitioners like me who think they can do SWE with one eye closed, "what's the big deal?") is also true.

antisthenes 5y ago

Your career path seems really interesting. Do you have a blog or some kind of work showcase where we can follow your journey and work?

lysecret 5y ago
happy to hear that. I fell in the trap of "to write a blog i have to code it myself haha" but il get back to you when its public:)

borroka 5y ago

Standard ML applications will be commoditized in the same way that creating a website has been commoditized. But for anything that isn't standard, and that's the vast majority of what businesses need, ML isn't going to become part of SWE's standard toolkit anytime soon. ML is kind of like tennis in the sense that you look at Nadal and Federer and all the greats and you can say, "man, I could do that," but you can't, not even close in this life and the next after this one. In fact, most SWEs who have developed some sort of "intuition" about ML have been quite dangerous, as they tend to be condescending to the real experts, have built things that make no sense at all or fall apart when 1 data point our of 100,000 changes, and when presented with the fact that they have no clue about ML, resort to the "the AI is all hype anyway" comeback. And vice versa (ML practitioners like me who think they can do SWE with one eye closed, "what's the big deal?") is also true.